Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.
Speaker 1: For the long holiday weekend, We're gonna try something a
Speaker 1: little different. My Bloomberg opinion colleague John Authors has begun
Speaker 1: an online book club. He looks at some of the
Speaker 1: classic and new books on the world of finance and investing.
Speaker 1: Joining us is Christine Harper. She is the editor in
Speaker 1: chief of Bloomberg Markets. John is the quintessential Englishman in
Speaker 1: New York. Before joining us at Bloomberg, he spent twenty
Speaker 1: nine years at The Financial Times. The first book in
Speaker 1: John series is from Peter Bernstein. For those of you
Speaker 1: who are not familiar with Peter Bernstein, he is the
Speaker 1: Michael Lewis of his day. He began writing in the
Speaker 1: sixties and seventies. The book that we talk about today
Speaker 1: is Capital Ideas, The Improbable Origins of Modern Wall Street.
Speaker 1: Bernstein published this in You probably are familiar with some
Speaker 1: of Bernstein's other work. I Just Adore Against the Gods.
Speaker 1: The Remarkable Story of Risk Uh probably his best known work,
Speaker 1: which tells about the rise of probabilities and statistics and
Speaker 1: actuarial tables and the insurance industry and why modern finance Oh,
Speaker 1: such a great debt of gratitude to the early people
Speaker 1: who were working on statistics and mathematics. You know, back
Speaker 1: in the day when ships would go overseas and try
Speaker 1: and come back with spices, it was a risky and
Speaker 1: dangerous venture. The ability to ensure those trips, the ability
Speaker 1: to figure out the odds of safe return and build
Speaker 1: in a reasonable um compensation for those who didn't come back, well,
Speaker 1: that was really really important, and that was developed by
Speaker 1: early statisticians, and probability for your risks. Eventually that becomes
Speaker 1: the modern insurance industry. John wanted to focus on a
Speaker 1: book of Peter Bernstein called Capital Ideas, The Improbable Origins
Speaker 1: of Modern Wall Street. It tells the history of how
Speaker 1: modern finance developed, and really it's quite fascinating, tracing the
Speaker 1: pioneering work of early scholars to the development of new
Speaker 1: theories on risk and valuation and returns. Really a very
Speaker 1: interesting book. So let's jump right into our conversation with
Speaker 1: John Authors and Christine Harper discussing Capital Ideas by Peter Bernstein.
Speaker 1: What made you guys start with this book? Given the
Speaker 1: what is it three hundred thousand books published annually plus
Speaker 1: the seven hundred thousand self published. Uh what what made
Speaker 1: you go back to Peter Bernstein as the first book
Speaker 1: to experiment with this? I feel it's very important actually
Speaker 1: to look not just at the books that have just
Speaker 1: come out, but at the books that have been that
Speaker 1: have come before, and that we may no longer have
Speaker 1: been paying attention to, because often there is information that's
Speaker 1: hidden there in in clear sight. I'll give you one
Speaker 1: anecdote to back that up. Donald steel of the Barlat
Speaker 1: and steel Um Investigative Journalism Partnership. They want a bunch
Speaker 1: of bulletz is for investigati journalism at the Philadelphia Inquirer
Speaker 1: came and gave a talk while I was at journalism
Speaker 1: school too many years ago to think about now. Somebody asked,
Speaker 1: where do you get most of your scoops from? Where's
Speaker 1: your main place you get your scoops? And we were
Speaker 1: all expecting from you know, underground, dark park car parking lots,
Speaker 1: from anonymous sources or whatever, and he said, from rereading
Speaker 1: my old notes, you find out you talk to people,
Speaker 1: you talk to twenty more people, and then when you
Speaker 1: go back to the first person you talked to, you
Speaker 1: realize now what the critical question is and that they
Speaker 1: answered it for you. And I then discovered I've not
Speaker 1: written that many books myself, but I've discovered precisely that
Speaker 1: has happened that on trying to unlock the key of
Speaker 1: a narrative, try to say something clearly, it's often going
Speaker 1: back to much earlier transcripts that had sort of said
Speaker 1: your treat become a sentiment in my mind, and that
Speaker 1: I hadn't actually re examined, actually gave me precisely the
Speaker 1: clarity answered the questions that I was asking again forgetting
Speaker 1: that I actually had the answer to them sitting in
Speaker 1: my notebooks. So that's one critical part of it. I
Speaker 1: feel very strongly that we shouldn't just be looking at
Speaker 1: the books have just come out. In the case of
Speaker 1: capital ideas, Partly it's very very good nobody was going
Speaker 1: to say this isn't a very well written book, um.
Speaker 1: And also I thought it was very important to look
Speaker 1: at these ideas written at a time before the crisis,
Speaker 1: because for the last ten years I'm as guilty of
Speaker 1: this as anybody else. The crisis was the most exciting
Speaker 1: period any of us who have been long term financial
Speaker 1: journalists or working finance have ever known, and for the
Speaker 1: last two years, it's been almost impossible to look at
Speaker 1: anything other than through the prism of that crisis. And
Speaker 1: that's particularly true of some of these ideas that are
Speaker 1: mentioned by by BENSTI I thought it would I thought
Speaker 1: it would might be very revealing to take a look
Speaker 1: at them, what people were saying about them. Isn't it
Speaker 1: true that whatever we're looking at, you know, the expression
Speaker 1: is every general fights the last war. Don't all market
Speaker 1: commentators and indeed traders and investors have their perspective colored
Speaker 1: by whatever the last big disaster was. We would still
Speaker 1: be talking about the dot com collapse and the crash
Speaker 1: of technology and why value does so much better than technology,
Speaker 1: but for the financial crisis intervening or am I overstating that?
Speaker 1: That's absolutely right? And I think one of the one
Speaker 1: of the things a lesson for me and I was
Speaker 1: journalists covering the financial industry during the crisis, and I
Speaker 1: think that very much colored my perspective on finance in
Speaker 1: that I saw the reliance on financial modeling and sort
Speaker 1: of statistical assumptions as flawed, given them flaws that were exposed.
Speaker 1: And what was valuable to me in reading capital ideas
Speaker 1: was recognizing in that book how important those models were
Speaker 1: and that they just haven't been with us all forever.
Speaker 1: They had to be created and they were very valuable.
Speaker 1: Maybe they were taken too far. I think people would
Speaker 1: mostly say they were, but without them we would be
Speaker 1: in equally as much trouble. So that everything is sort
Speaker 1: of a reaction to the last problem we had. And
Speaker 1: with capital ideas, there were too many people who just
Speaker 1: thought human judgment was the way to pick company stocks,
Speaker 1: and um, you know, they showed there's actually systemic you know,
Speaker 1: analysis that should go into it. But maybe we went
Speaker 1: too far and there was too much systemic analysis, not
Speaker 1: enough judgment. What what do you what models? Do you
Speaker 1: think we're pushed too far? And it's one thing if
Speaker 1: we talk about um, the Gaussian Coppla and things like that,
Speaker 1: but specifically these are really very basic ideas that eventually
Speaker 1: got implemented. It's kind of funny to read, oh, this
Speaker 1: is the person who figured out that reward is a
Speaker 1: function of risk. I just always assumed because it's been
Speaker 1: so fundamental to me, it's hard for me to imagine.
Speaker 1: So the parallel is, you know, kids today who play
Speaker 1: with iPads and technology and apps, there is no, online
Speaker 1: and offline. There just is if you're old enough, oh
Speaker 1: this is online, this is offline a six year old. No, No,
Speaker 1: that's just how the world is. It's digital, it's this,
Speaker 1: it's that. So to me, risk and reward have always
Speaker 1: been two sides of the same coin. It's hard to
Speaker 1: even imagine what it was like. Wait, people didn't know
Speaker 1: that that your reward was a fun shouldn't have how
Speaker 1: much risk youer storm? Right? And yet there there's this
Speaker 1: a kind of inherent belief that we can measure future
Speaker 1: risk by looking at you know, John has had a
Speaker 1: lot of online discussion about this. Volatility is telling you
Speaker 1: future risk and that helps to a point, but not
Speaker 1: maybe all the time. And then also liquidity, you know,
Speaker 1: the assumptions about liquidity that we're going on during the
Speaker 1: crisis or the build up to the crisis, we're very flawed.
Speaker 1: And so those those are two things that I think
Speaker 1: we're we're problematic. I think in finance, if we essue
Speaker 1: the word measure and replace it with the word estimate,
Speaker 1: we're much better off because hey, I'm guessing this is
Speaker 1: how much liquidity will be until it evaporates, or we're
Speaker 1: estimating how much risk there is. It creates a recognition
Speaker 1: that this isn't a hard and fest measure the old joke,
Speaker 1: why do why do economists for ideata to the third
Speaker 1: decimal point to demonstrate they have a sense of humor?
Speaker 1: How did How did people respond? I would say that
Speaker 1: we had two main areas where people had a problem,
Speaker 1: other than points of people pointing out particularly nice little
Speaker 1: gems of Bernstein wisdom. The first was that many people
Speaker 1: didn't quite grasp that risk in these models has been
Speaker 1: defined as volatility, and many of them felt, I think,
Speaker 1: quite accurately, that there is much more to what they
Speaker 1: think of as risk than the version of volatility that
Speaker 1: Sharp and others put into their their their their models.
Speaker 1: Let's put a pin in in right there and and
Speaker 1: digress a bit. Why do you believe people think volatility
Speaker 1: is equivalent to risk? It's, largely speaking, that's how it's defined.
Speaker 1: If you look in the models that in its final form,
Speaker 1: I guess Bill Sharp comes up with, and the Sharp ratio,
Speaker 1: which we're all used to as a risk adjusted return
Speaker 1: is is the return divided by the standard deviation, and
Speaker 1: so it's um And while it's plainly very important to
Speaker 1: grasp that you get money, you're you. You get money
Speaker 1: by taking a risk that if you really want to
Speaker 1: make yourself rich, you're going to have to take a chance.
Speaker 1: Um the risk that people are bothered about. For many institutions,
Speaker 1: it's about matching their liabilities, meaning the future obligations they
Speaker 1: have on a even surprisingly to know. Even endowments are
Speaker 1: worried about whether they can match their commitments for the future.
Speaker 1: And it's about to draw down. It's not about will
Speaker 1: this return waggle around a long way over the next
Speaker 1: thirty or forty years, but on balance particularly I don't
Speaker 1: have to access it all at one time. We're going
Speaker 1: to be fine. It's is it going to do very
Speaker 1: well for a long time and then crash and I'm
Speaker 1: never and subject me to a drawdown I never quite
Speaker 1: recover from. Uh. And that is much closer to the
Speaker 1: version of risk that people had in mind, and it's
Speaker 1: not the version of risk that's enshrined in in capem.
Speaker 1: The one from the book that I really liked was
Speaker 1: more things can happen than will happen, which is really
Speaker 1: a variation of the classic definition of risk, which is
Speaker 1: the possibility that your future expected returns will not be
Speaker 1: met and and that's a pretty simple definition as well,
Speaker 1: but they both sum up. Hey, you know, if markets
Speaker 1: return x a year, let's use ten percent over long
Speaker 1: periods of time. There are certain years where you're not
Speaker 1: going to get ten per and that's risk, at least
Speaker 1: to an investor who may need the money at a
Speaker 1: specific date in the future. Part of the issue, I
Speaker 1: think is that if you're an academic and you're trying
Speaker 1: to create a essentially a mathematical model, you need you
Speaker 1: need some number, some input that you can put in
Speaker 1: that model. And so what are you going to get
Speaker 1: to measure risk? What's the number? Volatility is a number,
Speaker 1: So maybe that's the closest you can come and it
Speaker 1: and it responds to John's reference of of drawdowns if
Speaker 1: you need if you have a specific liability at a
Speaker 1: future date, and right before that date, one of these big,
Speaker 1: ugly drawnouns of cars. Just look at what took place
Speaker 1: in the fourth quarter and the first quarter of en
Speaker 1: If you needed that money in December, you were in trouble.
Speaker 1: If you could wait till April, Hey, it's all everything's
Speaker 1: coming up roster. So it's not true risk, but it's
Speaker 1: volatility combined with liability. Is that that a fair statement?
Speaker 1: I think I think it is that sits following on
Speaker 1: from what's from what Christine just says. Yes, it's important
Speaker 1: to have a number, but there is a risk that
Speaker 1: you drift into the fallacy. This is a comment that
Speaker 1: all one person made you drift into the fallacy of
Speaker 1: the street light effect. I didn't know until now where
Speaker 1: the street light effect name comes from. It's about a
Speaker 1: drunk who is looking for his keys underneath the street lights,
Speaker 1: and the policeman asked, you're sure this is where you
Speaker 1: dropped them, and he says, well, no, but this is
Speaker 1: where I can see I mean, and that is I
Speaker 1: mean back to the financial crisis and what were some
Speaker 1: of the mistakes is that the data that was available
Speaker 1: on housing prices was plugged into everybody's risk model on
Speaker 1: you know, that's my second flag because we have multiple
Speaker 1: examples of enormous draw downs in housing prices which nobody
Speaker 1: wanted to answer into their system, the biggest clearly being
Speaker 1: the Great Depression, whereby some measures, New York City real estate,
Speaker 1: film value, and real estate collapsed around the country in price.
Speaker 1: But there are other examples in California in the nineteen
Speaker 1: eighties and New York. But but it wasn't the argument
Speaker 1: that nationally housing prices have. And I agree with you
Speaker 1: on the on the Great Depression, but they probably that's
Speaker 1: not gonna happen on average. But real estate is local.
Speaker 1: And the joke is if my head's in the oven
Speaker 1: and my feet are in the freezer on average, I'm comfortable. Well, okay,
Speaker 1: but I'll just say that the models that were employed
Speaker 1: did not use data. Yes, whether it was available or not,
Speaker 1: I agree, they chose to use recent data, which basically
Speaker 1: told them what they wanted to hear, so their free
Speaker 1: light was a little too narrow. That's that's exactly right.
Speaker 1: But but that goes to your point that models get
Speaker 1: pushed too far. And if it's garbage and garbage out.
Speaker 1: If we're only going to take an era, we're not
Speaker 1: gonna go out of sample, we're not gonna go into national,
Speaker 1: we're not gonna go back a hundred years. We're only
Speaker 1: going to use The ultimate back test is only use
Speaker 1: data that gives you the result that you want. And
Speaker 1: Burnstein makes that argument. I mean, he does say in
Speaker 1: this book that your your models are only good as
Speaker 1: good as the data you have, and so he was
Speaker 1: aware of that. But it's interesting how far the models
Speaker 1: were pushed without understanding that. One of the one other
Speaker 1: interesting thing is how much you see in the book
Speaker 1: how some of these models are based on simplifications which
Speaker 1: the people know they're making in order to be able
Speaker 1: to render it um u usable at all. Now, the
Speaker 1: one example I'm really thinking of here is Bill Sharpen
Speaker 1: the concept of beta, so that the idea, the idea
Speaker 1: that the whole, the whole basis of the valuation of
Speaker 1: a stock is based on its sensitivity to the market
Speaker 1: as it's one most important overriding factor. And the reason
Speaker 1: that he came up with that because yes, it is
Speaker 1: more probably that's the single most important factor in why
Speaker 1: most chair prices move. But the reason he came up
Speaker 1: with that is for for to make it calculable, because
Speaker 1: you had to have one overriding factor and then add
Speaker 1: on others thereafter. If you wanted to make the math doable,
Speaker 1: you need a number. And and you know, beta, particularly
Speaker 1: with the growth of passive investing, arguably is even more
Speaker 1: important than it used to be, but excus attention in
Speaker 1: ways that might be dangerous, and it helps it because
Speaker 1: it's such a nice, clear cut number. It gives us
Speaker 1: the comfort of a clear answer that we forget that
Speaker 1: it was based on what was knowingly a simplification when
Speaker 1: it first came out. The reason we are calculating things
Speaker 1: with the notion that beta is the single most important
Speaker 1: element of the return of a stock is because it
Speaker 1: was just too difficult to calculate if you were going
Speaker 1: to have a number of different factors. That chapter in
Speaker 1: a later chapter references the variance and the correlation between individuals,
Speaker 1: variants between different parts of portfolio, but also how margin
Speaker 1: visual stock will trade relative to the market. And something
Speaker 1: I have been told since I first started trading stocks
Speaker 1: a hundred years ago is that a third of the
Speaker 1: price movement is the stock, a third is the market,
Speaker 1: and a third is the sector that's referenced in the book.
Speaker 1: But nobody ever bothers to define that. Has that was
Speaker 1: that tested in here? Has anybody actually said, well, here
Speaker 1: are all the correlations? Are we just all taking that
Speaker 1: for granted? I think Farmer in French tested it did well.
Speaker 1: They did enormous empirical work which basically disproved the contention
Speaker 1: that market sufficient in my opinion, And yet reached the
Speaker 1: conclusion that markets were efficients, but that here were all
Speaker 1: these inefficiencies that could be explained somehow or other startastics.
Speaker 1: I don't, I don't quite. I think they did a
Speaker 1: brilliant piece of empirical work, and it's wonderful that they
Speaker 1: published something which basically suggested the original hypothesis is wrong. Well, well,
Speaker 1: I'm not quite sure that I agree with them that
Speaker 1: the original hypotheses is right. Nevertheless, you need a catchy
Speaker 1: nomencl you need a catchy title, and the mostly kind
Speaker 1: of sort of eventually efficient market hypothesis. I don't know
Speaker 1: if that's when any Nobel prizes, even though it's much
Speaker 1: more accurate. Yes, well, I mean the problem with the
Speaker 1: efficient market hypothesis that's it relies on a lot of
Speaker 1: people going in and trying to beat the market. Um,
Speaker 1: which we've seen and and some people let's talk about
Speaker 1: Professor Andrew Low and m I T. He's argued that
Speaker 1: you need five or six active market participants ninety plus
Speaker 1: percent can can index, and that should be enough to
Speaker 1: for for price discovery to work. Um, but what's in it?
Speaker 1: At some point? What's in it for the active market participants? Well? So,
Speaker 1: so now I'm gonna I'm just gonna keep dropping names.
Speaker 1: Michael mob Us on the Paradox of skill, effectively says
Speaker 1: that you it's not hard to beat the market because
Speaker 1: people are dumb. It's that there are so many smart
Speaker 1: talented people and there just isn't that much outpha to
Speaker 1: go around. So maybe if many of these smart talented
Speaker 1: people go away, it's less competitive. And I don't know
Speaker 1: where the number is ten percent, Andrew's five percent, those
Speaker 1: folks will have an opportunity to beat the market. And
Speaker 1: some people say that what people believe to be a scale,
Speaker 1: it's actually blind luck. So let's talk about Bernstein personally
Speaker 1: for a minute, because what you just said seems so
Speaker 1: much of an era that may or may not be
Speaker 1: valid any longer. I was surprised in the book to
Speaker 1: read him discuss his clients and his own participation in
Speaker 1: the market. I had no idea that he was a
Speaker 1: professional investor and an advisor. I assumed he was a
Speaker 1: professor and or a historian based on how well researched
Speaker 1: and written Um The Power of Gold and and Against
Speaker 1: the Gods. These read like academic books that are just
Speaker 1: beautifully written. So here's the question, for a person in
Speaker 1: a post war era where the you know, the thundering
Speaker 1: herd of Merrill Lynch and and all the different things
Speaker 1: that we're taking place. In the fifties, sixties, seventies, it
Speaker 1: seemed like stock ownership was moving from a rarefied wealthy
Speaker 1: person's hobby too fully democratized and here we are now
Speaker 1: back to bigger levels of of wealth inequality, and it
Speaker 1: seems what, what's the stat everybody likes to throw out
Speaker 1: the stocks and roun by the top ten percent something
Speaker 1: like that the top one percent owns so have was
Speaker 1: Bernstein of the moment in democratization and extrapolated that trend
Speaker 1: out forever? Did he? Did he? Or am I overstating that?
Speaker 1: And just I would just say, I mean one point
Speaker 1: he makes is that in you know, uh, capitalist countries
Speaker 1: like the US at the time he was writing, it's
Speaker 1: assumed that markets are good, and the countries that were
Speaker 1: coming out of the Soviet you know world like more.
Speaker 1: You know that Poland was very excited about having a
Speaker 1: stock market. So there has been a democratization of markets globally.
Speaker 1: So what's interesting, and we've documented a little bit in
Speaker 1: various articles we've written, is that if you look in
Speaker 1: some what you call developing markets around the world, they're
Speaker 1: very keenly developing these public markets, whereas some of the
Speaker 1: more sophisticated markets are now going into more private markets.
Speaker 1: And so I I just find that fascinating. Whether there's
Speaker 1: a sort of a the you know, a curve at
Speaker 1: which you know, societies go beyond private markets into this
Speaker 1: world of making privately owned companies more you know, widely
Speaker 1: owned or more of an investment, an opportunity. We have
Speaker 1: a very specific legislative history with the Jobs Act under
Speaker 1: President Bush that basically change the game for private companies.
Speaker 1: And it's not a coincidence that what is it a
Speaker 1: decade later we have all these unicorns billion plus doll
Speaker 1: evaluations that haven't gone public. Well, and you would have
Speaker 1: had no choice, but and you have you have you know,
Speaker 1: lots of pension funds that owned venture capital, they own
Speaker 1: private equity. Right, So in a way, even though it
Speaker 1: through multiple layers of institution, you have the average person
Speaker 1: owning pieces of non public companies, the average person through
Speaker 1: I mean public they employees through their own and others,
Speaker 1: you know you and yeah, it's still small. But I
Speaker 1: think the question is and I think a lot of
Speaker 1: people in the industry would say that's the future. I mean,
Speaker 1: you hear some quant traitors talk about bringing quant strategies
Speaker 1: to private equity. Um, I don't see why you couldn't
Speaker 1: have an overlay of quants onto anything that could be
Speaker 1: reduced to a mathematical basis. The problem with that is
Speaker 1: so much of this seems to be intuitive and um,
Speaker 1: what's the word I'm looking for, um, driven by human judgment.
Speaker 1: There are areas where machine learning and AI and technology
Speaker 1: are clearly supplanting human judgment. I am I'm not so
Speaker 1: sure that that a VC and and p E or
Speaker 1: the places where that's going to happen, although I could
Speaker 1: be completely wrong. Well, and there's those liquidity at you. Well,
Speaker 1: that's the always the liquidity premium for vench capital and
Speaker 1: private equity, and the same thing with gated withdrawals and
Speaker 1: and um, you know lock up periods for hedge funds.
Speaker 1: You end up with, is it purely a liquidity premium
Speaker 1: or is that just you know, something to create a
Speaker 1: little smoothing for managers. Not On the subject of liquidity,
Speaker 1: we've actually managed to come to the second big points
Speaker 1: that people made in their sponsors to um. There is
Speaker 1: very little. The word liquidity does not occur half as
Speaker 1: much as you think it's would in a book about
Speaker 1: all these fundamental ways in which people go about allocating
Speaker 1: their assets in public markets, and not unreasonably a lot
Speaker 1: of these these these people are geniuses that we're covering.
Speaker 1: Most of them at the time they were developing their
Speaker 1: theories had little or no experience of actually trading in markets.
Speaker 1: They were very interestedly looking at lots of data and
Speaker 1: bringing very fresh perspectives to it, and they were certainly
Speaker 1: operating in an era before the degree of liquidity that
Speaker 1: we have found in the recent years was possible, and
Speaker 1: therefore where the sudden changes in levels of liquidity of
Speaker 1: recent years was possible, and you could try to to say, well,
Speaker 1: you can just add liquidity as a factor. Ipotson's suggested
Speaker 1: that in recent years that low liquidity stocks could be
Speaker 1: a factor along the same lines as value or momentum
Speaker 1: or whatever. The cap size argument has been the premium
Speaker 1: you get for small cap over large isn't is in
Speaker 1: part a liquidly factor? Yes? But I think this is
Speaker 1: where the where people were nervous about the discussions of risk.
Speaker 1: The original risk models UM. The level of liquidity is
Speaker 1: very important and it's not exogenous. If things begin to
Speaker 1: go bad, there will be less liquidity. When there is
Speaker 1: less liquidity, the price will move more, and that will
Speaker 1: be get still more in liquidity UM. And We've had
Speaker 1: some very interesting quite technical, so I'm not going to
Speaker 1: try to summarize them, but interesting responses from options traders. Basically,
Speaker 1: I suppose the very simplified way of putting it is
Speaker 1: when UM, when volatility goes up, correlations go to one.
Speaker 1: You know, all their all their clever ideas disappear, you know,
Speaker 1: the various ways they've tried to protect against risk gets
Speaker 1: much more, much more prejudiced, much more compromised. When liquidity
Speaker 1: drives up. Everything comes down to the same thing. And
Speaker 1: the concept as well that when liquidity is drying up,
Speaker 1: you sell what you can sell, not what you think
Speaker 1: is worth can reach a higher price than it's worth.
Speaker 1: You sell just whatever you can sell if you need
Speaker 1: to sell something. So let's talk about three things. One
Speaker 1: is liquidly, one as correlations. But I but I have
Speaker 1: to bring up the portfolio insurance which was affected via
Speaker 1: options to be purchased in a crash. Now I know,
Speaker 1: I have the benefit of hindsight bias when I look
Speaker 1: back at this, But isn't it obvious that in the
Speaker 1: midst of a crisis, in the midst of a crash,
Speaker 1: buying put options that are in free fall are a
Speaker 1: not going to cover your shortfall and be gonna make
Speaker 1: the crash even worse. How did nobody point that out beforehand?
Speaker 1: And again, Bernstein has also the benefit of hindsight bias
Speaker 1: because he wrote this five years after the eighty seven crash. Yeah,
Speaker 1: I mean, it's just a good idea that was taken
Speaker 1: too far and was because I think in hindsight it
Speaker 1: sounds like a terrible I think if it were on
Speaker 1: a smaller scale, it wouldn't cover your losses. Well, it
Speaker 1: might not, It might not have driven things the way
Speaker 1: that that there was this sort of self uh, a
Speaker 1: flexive kind of quality to it that there was, so
Speaker 1: there was there the amount of portfolio insurance I was
Speaker 1: owned really too much added to the problem. So if
Speaker 1: if it was if it was done on a smaller scale,
Speaker 1: it might have been okay. And I wonder if if
Speaker 1: the seat belt effect applied, which is as we make
Speaker 1: safer and safer cars and at abs and air bags
Speaker 1: and seat belts. The death rate has fallen in plateaud
Speaker 1: and the only explanation that seems to make any senses well,
Speaker 1: people feel so much safer they drive faster regardless of conditions.
Speaker 1: So all these safety devices don't help us other than
Speaker 1: letting us behave a little more recklessly. Did portfolio insurance
Speaker 1: have the same impact? I think it's indeed, um what
Speaker 1: I would say if you. Because Bernstein was an extremely
Speaker 1: bright guy. Some of the comments he makes and the
Speaker 1: quotes he makes about the about Black Black Monday are
Speaker 1: very telling because they make clear um what the problem
Speaker 1: was and that the people who brought it up didn't
Speaker 1: really grasp it themselves. So Bernstein, and I'm quoting here
Speaker 1: that the shortfalling plan sales was a direct result of
Speaker 1: frenzied conditions that violated the underlying assumptions of portfolio insurance
Speaker 1: that ready buyers are always willing to accommodate the sellers
Speaker 1: in the insurance camp, which obviously was an assumption people forget,
Speaker 1: and liquidity is a fancy word for a ready buyers exactly.
Speaker 1: And and they realized that the problems of portfolio insurance
Speaker 1: in the crash were related to problems, and this is
Speaker 1: a quote from Haye Neyland. They realized that the problems
Speaker 1: of portfolio insurance in the crash were related to problems
Speaker 1: of market liquidity, not to some fundamental flaw of the
Speaker 1: underlying technique. If it's a problem with market liquidity, surely
Speaker 1: that is by definition, if it's a technique for buying
Speaker 1: and selling in the market, if probably with market liquidity undermines,
Speaker 1: it sounds fundamental to me. And then this glorious quote
Speaker 1: that Rubinstein, the other Leliand's partner in this the two
Speaker 1: gentlemen behind Flier Insurance who have taken a lot of
Speaker 1: the blame for Black Monday Um deservedly, Yes, bern Bernstein
Speaker 1: quotes into this effect. As a result, it was the
Speaker 1: market of that failed to provide conditions where portfolio insurance
Speaker 1: could work multi market, the market mass with the model.
Speaker 1: It's it's really stop and think about that rationalization, which
Speaker 1: brings me to something that that is related to the
Speaker 1: correlation and the volatility issue. So when when volatility spikes
Speaker 1: and all correlations go to one that very much hints
Speaker 1: at the behavioral issues of market participants, which as much
Speaker 1: as this book talks about a lot of theories. They
Speaker 1: all seem to predate the diverse Ky and Conman and
Speaker 1: Sailor and Chiller and go down the list. There is
Speaker 1: almost nothing in this book that says, hey, sometimes back
Speaker 1: to E. M. H And and Fama French, sometimes investors
Speaker 1: are just plumb crazy and do such stupid, self destructive
Speaker 1: things that all bets are off and you just have
Speaker 1: to wait for the smoke too. I think there there
Speaker 1: is an anecdote about black Sholes and how me Merton
Speaker 1: Black Controls tried to try to put their theory into
Speaker 1: practice and and bought some options and and did some trades,
Speaker 1: and they lost some money, and they realized that, Um,
Speaker 1: sometimes the market knows things that the models don't. So
Speaker 1: I wanna I want to ask Christina a question about
Speaker 1: something John said earlier that the two gentlemen who won
Speaker 1: the Pulitzers at UM Philadelphia Inquirer always go back to
Speaker 1: their notes and later on in the conversation it's what
Speaker 1: was written earlier but lacking context, um to be better understood.
Speaker 1: In the early part of the book, there's a quote
Speaker 1: from I'm sure I'm gonna mangle his name, Bachelia. How
Speaker 1: do you pronounce okay Um? I spent a very much
Speaker 1: less time in Paris than you did. Um, but I
Speaker 1: love this concept from It's so far ahead of the
Speaker 1: rest of the book, which is quote, the mathematical expectation
Speaker 1: of the speculator are zero. Now stop and think about that.
Speaker 1: It's it's foreshadowing what a zero sum game is in markets.
Speaker 1: It's foreshadowing indexing, it's foreshadowing passive over active. This is
Speaker 1: a hundred and twenty years ago. How on earth had
Speaker 1: did that just sit there and nobody noticed it for
Speaker 1: I don't know, almost a century. It's amazing. Yeah, and
Speaker 1: it and it it's and fuel every investors um insight
Speaker 1: into what they're doing every day. So we should just
Speaker 1: haven't taped to our wealth right right to your to
Speaker 1: your trading term. So given that we have all these
Speaker 1: PhDs today, we have all this work from Nobel Laureates
Speaker 1: that makes the canon of investing. What should investors be
Speaker 1: picking up from this book today? What's what's the takeaway
Speaker 1: that professional investors should be thinking about if they either
Speaker 1: read or reread this book. To me, it was the
Speaker 1: value of the economic ideas that have been um absorbed
Speaker 1: into the financial world and the limitations it's not just
Speaker 1: the value, it's the limitations. Hey, this is a better
Speaker 1: idea than before, but let's not get too far out
Speaker 1: of so you're not you shouldn't go with your gut,
Speaker 1: but you you shouldn't assume the model is gonna fill
Speaker 1: you with total confidence. But what are your thoughts? Do
Speaker 1: you do? You think that's fair a fair statement? Yes,
Speaker 1: sorry to be boring, and degree agree. But basically these
Speaker 1: models are good models given that they're trying to model
Speaker 1: something that all of us know from our lives. Trying
Speaker 1: to cover markets are almost impossible to to model. Um
Speaker 1: they are in most of the cases. There are things
Speaker 1: that when you're trying to work out how to allocate assets,
Speaker 1: pick a stock or whatever, you should at least take
Speaker 1: a look at the math of them. And if they
Speaker 1: are if they suggest that this would be a really
Speaker 1: bad idea, you should think very hard. They are You
Speaker 1: know that they are a systematized way to enable you
Speaker 1: to think rationally. They aren't the kind of infallible guide
Speaker 1: that some people felt them to be. And ultimately, the
Speaker 1: reason they became as unpopular as they did, because they
Speaker 1: were blamed h as much as they were for the crisis,
Speaker 1: is due to factors of overconfidence. They did feed in,
Speaker 1: among many other things, to this broad over confidence that
Speaker 1: allowed the crisis to happen. So so here's a couple
Speaker 1: of bullet points that I pulled out of the book
Speaker 1: that I thought were fascinating and and maybe maybe you
Speaker 1: guys can can share some thoughts on this. The first
Speaker 1: was I had no idea that before the nineteen fifties,
Speaker 1: if you had a trust or in a state that
Speaker 1: was being managed by a third party, the law was
Speaker 1: literally you can only have a fifty exposure to stocks
Speaker 1: and the rest had to be bonds or cash instruments.
Speaker 1: That was fascinating. Did did you did either of you
Speaker 1: before you read this book know that that was a
Speaker 1: substantial change in in how portfolios were managed. I I
Speaker 1: didn't realize that. But it makes sense in the wake
Speaker 1: of the depression that so ben those PTSD people are again,
Speaker 1: the generals are looking back and oh there can be
Speaker 1: a crash. So therefore no more than that was the
Speaker 1: guard rail. Well, again, it makes sense. I I covered
Speaker 1: Mexico for a while in the wake of the crisis.
Speaker 1: I had to cover the moments um more than a
Speaker 1: decade after Mexico's great financial crisis when they allowed the
Speaker 1: state's pension system to invest in anything other than bonds
Speaker 1: for the first time. It took huge negotiations with the
Speaker 1: unions before they would permit them to put put retirement
Speaker 1: money into anything other than bonds. But again, given what
Speaker 1: happened to Mexico in ninety four, you can understand why
Speaker 1: the unions thought this might be a bad idea. And
Speaker 1: I don't want to give short shrift to Paul Samuelson,
Speaker 1: John you just mentioned earlier, So let me ask this question,
Speaker 1: why did Samuelson's work send such shock waves through the
Speaker 1: regular community of investors. I think there's a beautiful anecdote.
Speaker 1: Um it's actually Sharp that has the conversation with Burn's
Speaker 1: been rather than Samuelson, but about Samuelson's work. That just
Speaker 1: Bernstein is talking to him about the investing he does
Speaker 1: h and Sharp says do you beat the market? And
Speaker 1: Bernstein is slightly offended at the time and also goes, well,
Speaker 1: how would I know? How would you judge? Which is
Speaker 1: astonishing in and of itself. It's that's less than fifty
Speaker 1: years ago that conversation happened, and that was the moment
Speaker 1: that Bernstein really started looking into all these ideas and
Speaker 1: taking them seriously. That was literally his epiphany right then,
Speaker 1: and they're sharp forced him down this bath. And and that,
Speaker 1: to answer your question, is also why people find found
Speaker 1: Samuelson so shocking. It seemed obvious that people who knew
Speaker 1: what they were doing would deliver value for you. The
Speaker 1: the idea that they actually could not not not not
Speaker 1: just did not could not add value in the act
Speaker 1: was just mind blowing. What's astonishing to me about that
Speaker 1: point in the book, which is actually fairly oh it's
Speaker 1: a Bill Sharp chapter. There was no performance reporting. No
Speaker 1: one said here's how we did this quarter, here's how
Speaker 1: our benchmark. The concept of benchmark did not exist. It's
Speaker 1: it's mind boggling, isn't that you deal with professional investors
Speaker 1: who live and die on their quarterly benchmark and complain
Speaker 1: about people trying to make them do monthly or weekly?
Speaker 1: Which is But isn't it interesting how far we've come
Speaker 1: that now? It's I think what's more commonly complained about
Speaker 1: now is how much people look at the benchmark instead
Speaker 1: of that, instead of looking at absolute returns. Right so
Speaker 1: that you have funds said brag about making a minus
Speaker 1: one percent return, you know, because the index was down
Speaker 1: and you know obviously that wouldn't have happened in the fifties.
Speaker 1: Well that and that that least the very strange concept. Um.
Speaker 1: I find this this, this, this, this is the point
Speaker 1: that somebody made me extraneously. After after reading this, you
Speaker 1: come to the what a lot of these ideas weaken
Speaker 1: is the concept the sense of ownership, so that now
Speaker 1: you can be in a position where you own a
Speaker 1: lot of a stock, but you're underweighted. So for example,
Speaker 1: if in Britain you more or less have to have
Speaker 1: more than ten percent of your portfolio each in BP
Speaker 1: and SHELL. So if you decide only to have five
Speaker 1: percent of your portfolio in BP, then you are underweighted.
Speaker 1: You own it, but you want it to do badly
Speaker 1: because you are betting that it will do badly even
Speaker 1: though your clients have five percent of their money. Right,
Speaker 1: it's about tilts, not absolute ownership. But but yeah, but
Speaker 1: the concept of if you own it, you're rooting for
Speaker 1: it has disappeared completely. I want to bring this forward
Speaker 1: to the modern era. There it was a quote um
Speaker 1: from Bernstein about industrials did not need as much capital
Speaker 1: as transports. That makes me wonder industrials did not need
Speaker 1: as much capital as transport. So if you're putting up
Speaker 1: a factory, you need need less money than the transports
Speaker 1: or the rails who had by rights of ways and
Speaker 1: and lay all of this track and then quote unquote
Speaker 1: rolling stock a phrase that you just don't here today. Um,
Speaker 1: So what does that mean about tech stocks today? If
Speaker 1: if Facebook brought Instagram for a billion dollars when it
Speaker 1: was I think nineteen or nine, maybe programmers um follow
Speaker 1: the progression. Rails and transports needed a ton of capital
Speaker 1: and a lot of labor. Industrials needed a little less
Speaker 1: capital and somewhat less labor. Modern tech companies need a
Speaker 1: whole lot less capital and just a handful of labor.
Speaker 1: What does that say about valuations based on the ideas
Speaker 1: in this book? Are we looking at perhaps a shift
Speaker 1: that has allowed pe multiples to climb for the past
Speaker 1: half century? Interesting question? Um. Certainly the notion ideas like
Speaker 1: like Topin's Q or trying to come to an intrinsic
Speaker 1: value that is based on assets, need to be revised
Speaker 1: with the notion of very much. For the notion of intangibles,
Speaker 1: Intellectual property is different than factories and equipment there is
Speaker 1: a concept that Bruce Greenwald, who I was taught by
Speaker 1: my MBI that Columbia uses of franchise value and of
Speaker 1: earnings power. I that that Facebook may not have a
Speaker 1: lot of capital tied up in a lot of workers working,
Speaker 1: but it does have a certain amount of franchise power,
Speaker 1: which conceivably is weakening as we speak. But that's that
Speaker 1: is the measure you somehow have to hear the mot Yes,
Speaker 1: always we need to have a moat around your business
Speaker 1: and prevents competition. So so that was the first question,
Speaker 1: and I think you've you've sort of I don't know
Speaker 1: if there's an answer, but at least but I think,
Speaker 1: I mean, it's interesting that just this idea that a
Speaker 1: company's ability to access capital should be dependent on its need, right,
Speaker 1: because now I think people will give capital to companies
Speaker 1: that don't need it so much and then just a
Speaker 1: lot of executives get paid a lot of money. Well,
Speaker 1: there is that with all the buybacks. We just saw
Speaker 1: Netflix raise another two billion dollars at five percent, because hey,
Speaker 1: content is expensive. Um Uber has had no problem raising
Speaker 1: capital even though they've just burned through a ton of it.
Speaker 1: You could you could go down the list of of
Speaker 1: tech companies and unicorns that are so heavily cap Look
Speaker 1: at we Works, which just filed to go public. They
Speaker 1: bought the Lord and Taylor flagship here in New York,
Speaker 1: which is a giant block long um department store. The
Speaker 1: issue of capital flowing to places where maybe it'll be repaid,
Speaker 1: maybe it won't. It's kind of shocking, isn't it. What
Speaker 1: what does that say about that? But the question I
Speaker 1: really have to ask, So the book has written in
Speaker 1: all of the academics Harry Marko, It's Bill Sharp, Gene Fama,
Speaker 1: go down the list. Are all born you know, either
Speaker 1: certainly before World War two for the most part. In fact,
Speaker 1: I want to say just about everybody was born before
Speaker 1: World War two, and most of them did most of
Speaker 1: their work in their twenties, thirties, forties, which raises the
Speaker 1: question is there a person born after who one day
Speaker 1: might be mentioned alongside of them? Has has all the
Speaker 1: low hanging academic fruit been picked? And this is going
Speaker 1: to be the pantheon? Or are new up and comers
Speaker 1: coming about who you know from the world of millennials?
Speaker 1: Are there going to be any academics who can put
Speaker 1: out work of this stature and this influence when you
Speaker 1: mentioned the behavioral economists for sure, so I would none
Speaker 1: of whom are under fifty. Okay, Um, well, Andrew Lowe,
Speaker 1: who is also beyond fifty at this point. But Andrew
Speaker 1: low what he's attempting to do, which has come up
Speaker 1: with an adaptive markets hypothesis that is advance. Yes, I'm
Speaker 1: not sure he's quite managed to do it, although he's
Speaker 1: written fascinatingly about his attempts to get there. If somebody
Speaker 1: does get there, that would be very interesting. Indeed. So
Speaker 1: that's my conversation with John Author's he's a colleague at
Speaker 1: Bloomberg Opinion, and Christine Harper, she's the editor in chief
Speaker 1: of Bloomberg Markets mag zine, about Peter Bernstein's book Capital Ideas,
Speaker 1: the Improbable Origins of Modern Wall Street. If you enjoyed
Speaker 1: this conversation, we'll be sure and look Up an Inch
Speaker 1: or Down an Inch on Apple iTunes and you could
Speaker 1: see any of the other two hundred and fifty such
Speaker 1: conversations we've had over the past five years. July twelfth
Speaker 1: is our five year podcast anniversary, so be sure in
Speaker 1: swing by and check out some of the special features.
Speaker 1: We will be running that week. I would be remiss
Speaker 1: if I did not thank the Crack staff that helps
Speaker 1: us put together these podcasts every week. Robert Bragg is
Speaker 1: my audio engineer. Attica val Brunn is my project director.
Speaker 1: Michael Boyle is my producer slash booker. Michael Batnick is
Speaker 1: our head of research. I'm Barry Rehults. You've been listening
Speaker 1: to Masters in Business on Bloomberg Radio